CN107578365A - Small echo digital watermark embedding and extracting method based on quantum weeds optimizing mechanism - Google Patents
Small echo digital watermark embedding and extracting method based on quantum weeds optimizing mechanism Download PDFInfo
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Abstract
The invention provides a kind of small echo digital watermark embedding and extracting method based on quantum weeds optimizing mechanism, belong to Information Hiding Techniques field.Specific implementation process is：Binaryzation is carried out to watermarking images, and the watermarking images after binaryzation are encrypted.Watermarking images after carrier image and encryption are transformed in wavelet field, it is divided into multiple embedded points in carrier image, according to the different parameters of quantum weeds optimizing mechanism optimization, watermark is embedded in using additivity or multiplicative updates, the insertion of time domain completion watermark is then transformed to by wavelet reconstruction；The extracting method of watermark is corresponding with embedding grammar, wavelet field is transformed to containing watermarking images, the watermark after different embedded points extracts scrambling encryption according to different parameters, it is integrated into complete watermark, it is then converted in time domain, the watermark extracted by scrambling resumption.Compare with existing method, the not sentience, robustness and security of this method are obtained for raising, with more practicality.
Description
Technical field
The invention provides a kind of small echo digital watermarking multiple spot based on quantum weeds optimizing mechanism is embedded and extracting method,
Belong to Information Hiding Techniques field.
Background technology
Digital image watermarking technology is an important directions of Information Hiding Techniques, continuous with computer process ability
Enhancing, the ability to break a code improve constantly, traditional encryption technology faces enormous challenge.On the other hand, with computer
The popularization of communication network and Internet technology, multimedia digital rich choice of products daily life, but simultaneously as network
The opening and sharing of transmission, multimedia digital product are very easy to be replicated, distorted, or even are maliciously deleted, numeral production
The copyright problem of product is extremely urgent.From nineteen ninety-five, since digital watermarking comes into vogue, the research of digital watermarking achieves preferably
Achievement in research.In early days, the research of digital watermarking concentrates on spatial domain watermark, and the ability of its anti-geometric distortion is strong, but embedded information
Amount is few.At present, the research of watermark is concentrated mainly on transform domain, and the insertion of watermark is completed by changing coefficient in transform domain.In document
Existing transform domain mainly includes：Discrete cosine transform (DCT), discrete Fourier transform (DFT), wavelet transform (DWT),
Hadamard (Hadamard) conversion, KLT conversion etc..Wherein DCT, DFT, DWT are the most commonly used, and wavelet transform is unique due to it
Time frequency analysis characteristic, can not only preferably be matched with human visual system (HVS), and and JPEG2000, MPEG4 it is isobaric
Contracting standard is compatible.Therefore the wavelet field for watermark being embedded into carrier image can obtain good not sentience and preferable robust
Property.
Found by the retrieval to prior art literature, WANG Xiaohong, Sun Ye is waited by force《Photoelectron laser》(2017,
Vol.28, No.4, pp.419-426) on profit in " the area-of-interest watermarking algorithm decomposed based on QR codes and Schur " delivered
Human visual system (HVS) notable figure is decomposed and met with the relatively low Schur of time complexity, is taken by notable figure normalization
Embedment strength is used as after anti-, adaptive is embedded into the watermark after encryption carrier image through Schur points of wavelet transformation and piecemeal
In the coefficient of solution.This method makes the robustness of watermark be improved, but the not sentience of watermark has declined；Mashruha
Raquib Mitashe etc. exist《2017IEEE Internation Conference Image, Vision＆Pattern
Recognition》" the A adaptive digital image delivered on (13-14, Feb.2017, pp.1-5)
Watermarking scheme with PSO, DWT and XFCM " propose it is a kind of based on improve fuzzy C-means clustering from
Digital figure watermark model, the models coupling particle swarm optimization algorithm and fuzzy C-means clustering is adapted to carry out in advance carrier image
Processing, select the insertion watermark of host image suitable position fragment, using this method insertion watermark can not perceptible aspect take
Certain effect was obtained, but its robustness is not significantly improved.
Existing data shows that the not sentience and robustness of digital figure watermark are that a pair of conflicting performances refer to
Mark.Conventional digital image water mark method is all based on the selection of many experiments experience and is appropriately embed intensity and embedded location insertion water
Print, this method has certain effect, but spent material resources and manpower are very huge.For this complexity system of watermark
System, some adaptive algorithms proposed in recent years are often absorbed in the not sentience and robustness one party for improving watermaking system
The performance in face, it is impossible to effectively take into account both unifications, the certain methods of proposition are suitable only for certain fields, and engineering application value is not
It is high.Therefore, a kind of small echo digital watermark embedding and extracting method based on quantum weeds optimizing mechanism are proposed in this patent.
The not sentience and robustness of digital figure watermark are considered in this method simultaneously, by reasonably selecting both object functions
Weight coefficient, a continuous multi-objective optimization question is converted into single-object problem, by selecting multiple embedded points,
By the scattered different zones for being embedded into carrier image wavelet transformed domain of watermark information, and incorporating quantum calculates and colony intelligence pursuit airplane
System devises quantum weeds optimizing mechanism, and adaptive being realized to multiple embedding parameters optimizes.The insertion that last basis has optimized
Parameter realizes that the multiple spot of digital picture is embedded and extracts.Simulation results show, the small echo based on quantum weeds optimizing mechanism
Digital watermark embedding and extracting method have more preferable not sentience, and being made an uproar in Gaussian noise and the spiced salt compared with existing method
Under acoustic environment, watermark robustness that small echo digital watermark embedding and extracting method based on quantum weeds optimizing mechanism extract
It is stronger.
The content of the invention
The invention provides a kind of small echo digital watermark embedding and extracting method based on quantum weeds optimizing mechanism, its mesh
Be provide that a kind of not sentience based on high, fast convergence rate the quantum weeds optimizing mechanism of convergence precision is strong, robust
The high small echo digital watermark embedding of property and extracting method.
The object of the present invention is achieved like this：
Step 1, binaryzation watermarking images go forward side by side line shuffle encryption.To watermark binaryzation, watermarking images can be effectively reduced
Redundant data, strengthen the not sentience of watermark.
Step 2, wavelet transformation is carried out to the watermarking images after carrier image and scrambling encryption.
Step 3, the multiple spot insertion of encrypted watermark.
Step 4, the multiple spot extraction of watermark and scrambling resumption.
Step 5, initialize quantum weed population.
Step 6, calculate the fitness of every plant of quantum weeds.
Step 7, growth and breeding.
Step 8, space diffusion.
Step 9, calculate the fitness of new caused quantum weeds, competition exclution.Calculated according to step 3 and step 4 new
The fitness of caused quantum weeds.
Step 10, judge whether to meet end condition, if satisfied, then stopping, exporting optimal quantum weeds, be designated as Gb, it is no
Then iterations adds 1, return to step seven.
Step 11, by GbMapping obtains corresponding embedment strength, embedded location, embedded length and section length, performs
Step 1: two and three embedded watermark, obtain being embedded in the image after watermark；Watermark is extracted according to step 4.
Incorporating quantum of the present invention calculates and bionical swarm intelligence, devises quantum weeds intelligent search mechanism, examines simultaneously
Consider the not sentience and robustness of digital picture watermarks system, and watermark not sentience is being improved using multiple spot embedded mode
While with robustness, the security of watermark information is improved.
Compared with prior art, method provided by the invention has advantages below：
(1) present invention not only increases the not sentience of digital figure watermark, and improves its robustness, digitized map
As watermaking system performance is improved, with more practicality.
(2) the quantum weeds Optimization Mechanism that the present invention designs simplifies former weed invasion method Evolution Strategies, avoids numerous
Miscellaneous parameter setting, there is the advantages that parameter setting is simple, and amount of calculation is few.
(3) digital figure watermark multiple spot insertion provided by the invention and extracting method, not only make digital picture watermarks system
Do performance to improve, and make it that the decryption of watermark is more complicated, enhance the security of watermark.
Brief description of the drawings
Small echo digital watermarking multiple spot insertion and extracting method schematic diagram of the Fig. 1 based on quantum weeds.
Image (Fig. 2 .b), original watermark image after the insertion watermark that Fig. 2 carrier images (Fig. 2 .a), the present invention obtain
The watermark that the present invention extracts under watermarking images (Fig. 2 .d) and noise-free environment after (Fig. 2 .c), Arnold scrambling encryptions
(Fig. 2 .e).
Fig. 3 is not added with the case of attacked by noise, and (Fig. 3 .c) of the invention is based on particle swarm optimization algorithm (Fig. 3 .b) with existing
And the small echo digital figure watermark single-point insertion of genetic algorithm (Fig. 3 .a) and the not sentience contrast simulation knot of extracting method
Fruit.
It is (Fig. 4 .c) of the invention and existing based on particle swarm optimization algorithm (figure when Fig. 4 Gaussian noises intensity is 0.0001
4.b) and genetic algorithm (Fig. 4 .a) small echo digital figure watermark single-point insertion and extracting method robustness contrast simulation knot
Fruit.
When Fig. 5 salt-pepper noises intensity is 0.01, of the invention (5.c) and existing particle swarm optimization algorithm (Fig. 5 .b) is based on
And the small echo digital figure watermark single-point insertion of genetic algorithm (5.a) and the robustness contrast simulation result of extracting method.
Fig. 6 iterationses 100 times, independent repeated trials 100 times, it is of the invention and it is existing based on particle swarm optimization algorithm and
The iterativecurve comparison diagram that the small echo digital figure watermark single-point insertion of genetic algorithm and extracting method obtain.
Fig. 7 iterationses 100 times, independent repeated trials 100 times, the present invention and the small echo based on invasive weed optimized algorithm
The iterativecurve comparison diagram that digital figure watermark multiple spot is embedded in and extracting method obtains.
Embodiment
Method provided by the invention is specifically described below：
Step 1, binaryzation watermarking images go forward side by side line shuffle encryption.To watermark binaryzation, watermarking images can be effectively reduced
Redundant data, strengthen the not sentience of watermark.If the coefficient matrix of the watermarking images after binaryzation is W, size isCombining encryption technology does scrambling encryption to the watermarking images coefficient matrix W of binaryzation, makes the information of watermark unreadable,
Increase the security of watermark.
Step 2, wavelet transformation is carried out to the watermarking images after carrier image and scrambling encryption.If the coefficient of carrier image
Matrix is V, and size is M × M.K (K >=3) level wavelet decomposition is carried out to V and obtains the wavelet coefficient sequence V of carrier image0.Wherein
Low frequency, level, vertical and four sub-bands of high frequency are all can obtain per level of decomposition, and low frequency sub-band can continue to decompose, altogether
3K+1 sub-band can be obtained.The low frequency that the high frequency and K levels decomposed except the first order decomposes, its sub-bands is intermediate frequency
Band, note midband wavelet coefficient sequence is Vmid.K ' (K ' ＜ K) level wavelet transformation is carried out to the coefficient matrix after scrambling encryption to obtain
The wavelet coefficient sequence W of watermarking images after to scrambling encryption0。
Step 3, the multiple spot insertion of encrypted watermark.Put and be embedded in for N (N >=2), in carrier image midband wavelet coefficient
Sequence VmidAccording to embedded location T=[T1,T2,...,TN] and section length m=[m1,m2,...,mN] it is divided into N sections, wherein the
J (j=1,2 ..., N) section VjRepresent that from original position label be TjThe m of beginningjThe sequence of individual wavelet coefficient composition.According to insertion
Length L=[L1,L2,...,LN],Lj＜ mj(j=1,2 ..., N) the wavelet coefficient sequence W of watermarking images after scrambling encryption0
It is divided into N sections, is designated as Wj(j=1,2 ..., N).The embedding grammar of watermark uses additivity rule V 'j=Vj+αj·WjOr multiplying property method
Then V 'j=Vj(1+αj·Wj), wherein αjFor the embedment strength of jth section watermark.V 'jAgain it is spliced into V 'mid, pass through small echo weight
Structure is designated as V ' to the image after embedded watermark, its coefficient matrix, the similarity table of image and carrier image after embedded watermark
Show as follows
Wherein Fxor() represents XOR function, Vx,y,Vx′,yCarrier image coefficient matrix is in x rows y before and after representing embedded watermark
The value of row.Sign () represents to take sign function.
Step 4, the multiple spot extraction of watermark and scrambling resumption.K level wavelet decompositions are carried out to the image after embedded watermark,
According to the embedded location T of watermark, embedded length L, section length m in its midband, in corresponding position according to W 'j=(V 'j-
Vj)/αjOr W 'j=(V 'j/Vj-1)/αj, the watermark wavelet coefficient sequence after encryption is extracted, is extracted by scrambling resumption
Watermark information.Watermarking images are obtained by K ' level wavelet reconstructions.Its coefficient matrix is designated as W ', the watermarking images extracted and
The similarity of the watermarking images of binaryzation represents as follows
Wherein Wx,y,W′x,yCarrier watermark and the value that the watermarking images coefficient matrix extracted arranges in x rows y are represented respectively.
Step 5, initialize quantum weed population.Quantum weed population scale Mmax, maximum iteration tmax, due to this
The parameter being related in method design has：Segments N, embedment strength vector α, embedded location vector T, each section of watermarking images wavelet systems
Several embedded length vector L, the section length vector m of each section of carrier image wavelet coefficient.So put for more (N >=2) embedding
Enter, the i-th (i=1,2 ..., M in t generationsmax) strain quantum weedsIt is expressed as
WhereinWithRespectively N
Duan Shuiyin embedment strength, embedded location quantum state corresponding with embedded length,It is N section carrier figures
As section length corresponding to quantum state.The embedment strength of jth (j=1,2 ..., N) section watermark and the specific of its quantum state are reflected
The relation of penetrating is
The specific mapping relations of the corresponding quantum state of embedded location are
The specific mapping relations of the corresponding quantum state of embedded length are
The specific mapping relations of the corresponding quantum state of section length are
Wherein Fround() represents bracket function nearby.un,ln(n=1,2 ..., 4N) represent the n-th bound for tieing up variable.
Step 6, calculate the fitness of every plant of quantum weeds.According to mapping relations, by i-th plant of quantum weedsResiding
Quantum state is mapped as the embedding parameter of N points insertionRoot
According to step three and four, its f is calculated1And f2Value, be designated asWithThe fitness of i-th plant of quantum weeds plant is
Wherein 0 ＜ δ ＜ 1 represent weight coefficient, are constant, not sentience and robustness can be adjusted by changing δ size
Shared proportion.Current quantum weeds are ranked up according to fitness value, wherein the maximum quantum weeds plant quilt of fitness
It is labeled asRepresent the optimal quantum weeds plant in current population.Table
Show t on behalf of only MmaxThe set that the optimal quantum weeds of memory of/4 plants of quantum weeds are formed, is initialized as when iteration starts
The maximum preceding M of current fitness valuemax/ 4 plants of quantum weeds.Wherein(i=1,2 ..., Mmax/4)
Represent i-th plant of quantum weeds to t on behalf of the optimal quantum weeds of memory only.
Step 7, growth and breeding.In order to avoid the calculating of too many redundancy, P is only designated as in populationtQuantum weeds can be with
Produce seed.Filial generation quantum weeds in the maximum filial generation quantum weeds of fitness be designated asRepeatedly
Generation is current quantum weeds when starting.Seed number caused by every plant of quantum weedsDetermined by simulating Quantum rotating gate, wherein,
The transient state quantum anglec of rotation corresponding to the jth dimension variable of i-th plant of quantum weeds in t generationsIt is defined as
J=1,2 ..., 4N wherein c1,c2,c3,c4It is fixed constant for twiddle factor, r1,r2It is equal between [0,1]
Even random number, the corresponding transient state quantum state of the jth dimension variable of i-th plant of quantum weeds are expressed as
Transient state quantum state residing for i-th plant of quantum weeds plant is defined as
Its caused seed numberBy rightMeasurement obtains：
r3For the random number between [0,1].The design considers all variables of quantum weeds plant to its fitness value
Percentage contribution, more fully using existing information, reduce amount of calculation, while make population scale in [Mmax/2,Mmax] between
Dynamic adjusts, and adds the diversity of population.
Step 8, space diffusion.Filial generation quantum weeds are by caused by the flooding mechanism of space.Quantum weeds utilize simulation
The space diffusion that Quantum rotating gate completes quantum weeds produces new quantum weeds, wherein the of i-th plant of quantum weedsThe quantum rotation angle of the jth dimension variable of strain filial generation quantum weedsIt is defined as
Corresponding quantum state is expressed as
Wherein c1,c2,c3,c4,c5,c6It is fixed constant for twiddle factor, r4,r5,r6Between [0,1] it is uniform with
Machine number, r are [0, Mmax/ 4] random integers between,Represent residing for the jth dimension of r strain quantum weeds in parent quantum weeds
Quantum state.
Step 9, calculate the fitness of new caused quantum weeds, competition exclution.Calculated according to step 3 and step 4 new
The fitness of caused quantum weeds.For i-th plant of quantum weeds and its filial generation quantum weeds, first filial generation quantum weeds each other
Competition, the maximum filial generation quantum weeds of fitness, which are retained, to be denoted asOrderThenWithCompetition, if
Fitness value be more thanFitness value, thenInstead ofAs parent weeds of new generationOtherwiseIt is updated toIt is allAfter completing renewal, finallyThe maximum quantum weeds renewal G of middle fitnesst+1。
Step 10, judge whether to meet end condition, if satisfied, then stopping, exporting optimal quantum weeds, be designated as Gb, it is no
Then iterations adds 1, return to step seven.
Step 11, by GbMapping obtains corresponding embedment strength, embedded location, embedded length and section length, performs
Step 1: two and three embedded watermark, obtain being embedded in the image after watermark；Watermark is extracted according to step 4.
The insertion of small echo digital figure watermark multiple spot and extracting method provided by the invention based on quantum weeds algorithm are imitated
True parameter setting is as follows：Carrier image is 256 × 256 eight grayscale images, and carrier watermark is 32 × 32 eight gray scales
Level image, to simplify computation complexity, section length and embedded length take fixed value according to abundant experimental results.Using
Arnold conversion carries out scrambling encryption.In experiment using 2 points insertion, first paragraph embedded location section for [10240,128 ×
128], section length 1024, embedded length 256, embedment strength section are [0,1], and second segment embedded location section is
[1024,10240-3072], section length 3072, embedded length 256 × 3, embedment strength section be [0,1] be l=[0,0,
10240,1024], u=[1,1,128 × 128,10240-3072], m=[1024,3072], L=[256,256 × 3]；Respectively
K=3 and K '=1 grade Haar wavelet transformations, quantum weeds algorithm phase are carried out to the watermarking images after carrier image and scrambling encryption
Related parameter is arranged to：Population scale is 20, iterations 100, weight coefficient σ=0.5, rotation shadow factor c1=0.03, c2=
0.06,c3=0.02, c4=0.04, c5=0.01, c6=0.02.
Simulation result shows that the multiple spot of the small echo digital watermarking proposed by the present invention based on weeds optimizing mechanism is embedded and carries
Take method to improve the not sentience of digital picture watermarks system, while also improve robustness, and safety
Property.
Claims (9)
1. a kind of small echo digital watermark embedding and extracting method based on quantum weeds optimizing mechanism, it is characterised in that：(1) two-value
Change watermarking images go forward side by side line shuffle encryption；(2) wavelet transformation is carried out to the watermarking images after carrier image and scrambling encryption；(3)
The multiple spot insertion of encrypted watermark；(4) the multiple spot extraction of watermark and scrambling resumption；(5) quantum weed population is initialized；(6) calculate
The fitness of every plant of quantum weeds；(7) growth and breeding；(8) space is spread；(9) fitness of new caused quantum weeds is calculated,
Competition exclution, the fitness of quantum weeds according to caused by step 3 and step 4 calculating are new；(10) judge whether to meet to terminate
Condition, if satisfied, then stopping, optimal quantum weeds are exported, are designated as Gb, otherwise iterations add 1, return (7)；(11) by GbReflect
Penetrate to obtain corresponding embedment strength, embedded location, embedded length and section length, perform (1), (2) and (3) and be embedded in watermark, obtain
Image to after embedded watermark, watermark is extracted according to (4).
2. a kind of small echo digital watermark embeddings based on quantum weeds optimizing mechanism of according to claim 1 and extraction side
Method, it is characterised in that：The process of described wavelet transformation is, if the coefficient matrix of carrier image is V, size is M × M, and V is entered
Row K (K >=3) level wavelet decomposition obtains the wavelet coefficient sequence V of carrier image0, each of which level decompose all can obtain low frequency, water
Flat, vertical and four sub-bands of high frequency, and low frequency sub-band can continue to decompose, and can obtain 3K+1 sub-band altogether, except
The low frequency that the high frequency and K levels that the first order is decomposed decompose, its sub-bands is midband, and note midband wavelet coefficient sequence is
Vmid, the small of the watermarking images after scrambling encryption is obtained to coefficient matrix progress K ' (K ' ＜ K) level wavelet transformation after scrambling encryption
Wave system Number Sequence W0。
3. a kind of small echo digital watermark embeddings based on quantum weeds optimizing mechanism of according to claim 1 and extraction side
Method, it is characterised in that：Described multiple spot telescopiny is to put and be embedded in for N (N >=2), in carrier image midband wavelet coefficient
Sequence VmidAccording to embedded location T=[T1,T2,...,TN] and section length m=[m1,m2,...,mN] it is divided into N sections, wherein the
J (j=1,2 ..., N) section VjRepresent that from original position label be TjThe m of beginningjThe sequence of individual wavelet coefficient composition, according to insertion
Length L=[L1,L2,...,LN],Lj＜ mj(j=1,2 ..., N) the wavelet coefficient sequence W of watermarking images after scrambling encryption0
It is divided into N sections, is designated as Wj(j=1,2 ..., N), the embedding grammar of watermark use additivity rule Vj'=Vj+αj·WjOr multiplying property method
Then Vj'=Vj(1+αj·Wj), wherein αjFor the embedment strength of jth section watermark, Vj' V is spliced into againm′id, pass through small echo weight
Structure is designated as V ' to the image after embedded watermark, its coefficient matrix, the similarity table of image and carrier image after embedded watermark
Show as follows
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4. a kind of small echo digital watermark embeddings based on quantum weeds optimizing mechanism of according to claim 1 and extraction side
Method, it is characterised in that：The multiple spot extraction of described watermark and scrambling resumption process are to carry out K levels to the image after embedded watermark
Wavelet decomposition, according to the embedded location T of watermark, embedded length L, section length m in wherein frequency band, corresponding position according to
Wj'=(Vj′-Vj)/αjOr Wj'=(Vj′/Vj-1)/αj, the watermark wavelet coefficient sequence after encryption is extracted, passes through scrambling resumption
The watermark information extracted, watermarking images are obtained by K ' level wavelet reconstructions, its coefficient matrix is designated as W ', the water extracted
The similarity of the watermarking images of watermark image and binaryzation represents as follows
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Wherein Wx,y、W′x,yCarrier watermark and the value that the watermarking images coefficient matrix extracted arranges in x rows y are represented respectively.
5. a kind of small echo digital watermark embeddings based on quantum weeds optimizing mechanism of according to claim 1 and extraction side
Method, it is characterised in that：The process of described initialization quantum weed population is quantum weed population scale Mmax, greatest iteration time
Number tmax, because the parameter being related in this method design has：Segments N, embedment strength vector α, embedded location vector T, each section of water
Embedded length vector L, the section length vector m of each section of carrier image wavelet coefficient of watermark image wavelet coefficient, so, for more
The insertion of (N >=2) point, the i-th (i=1,2 ..., M in t generationsmax) strain quantum weedsIt is expressed as
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</msubsup>
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<mn>4</mn>
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</msubsup>
<mo>&rsqb;</mo>
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<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
WhereinWithRespectively N sections water
Embedment strength, the embedded location quantum state corresponding with embedded length of print,It is N section carrier images
Quantum state corresponding to section length, the embedment strength of jth (j=1,2 ..., N) section watermark close with the specific mapping of its quantum state
It is to be
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The specific mapping relations of the corresponding quantum state of embedded location are
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<mn>5</mn>
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</mrow>
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The specific mapping relations of the corresponding quantum state of embedded length are
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<mi>L</mi>
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The specific mapping relations of the corresponding quantum state of section length are
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Wherein Fround() represents bracket function nearby.un,ln(n=1,2 ..., 4N) represent the n-th bound for tieing up variable.
A kind of 6. small echo digital watermark embeddings based on quantum weeds optimizing mechanism of according to claim 1
And extracting method, it is characterised in that：The fitness process of described every plant of quantum weeds of calculating is, according to reflecting
Relation is penetrated, by i-th plant of quantum weedsResiding quantum state is mapped as the embedding parameter of N points insertionAccording to the process of (3) and (4), its f is calculated1And f2's
Value, is designated as f1 iWithThe fitness of i-th plant of quantum weeds plant is
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<mo>-</mo>
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<mrow>
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<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein 0 ＜ δ ＜ 1 represent weight coefficient, are constant, changing δ size can adjust shared by not sentience and robustness
Proportion, current quantum weeds are ranked up according to fitness value, the wherein maximum quantum weeds plant of fitness is labeled
ForRepresent the optimal quantum weeds plant in current population.Represent
T is on behalf of only MmaxThe set that the optimal quantum weeds of memory of/4 plants of quantum weeds are formed, is initialized as current when iteration starts
The maximum preceding M of fitness valuemax/ 4 plants of quantum weeds, wherein(i=1,2 ..., Mmax/ 4) represent
I-th plant of quantum weeds is to t on behalf of the optimal quantum weeds of memory only.
7. a kind of small echo digital watermark embeddings based on quantum weeds optimizing mechanism of according to claim 1 and extraction side
Method, it is characterised in that：The process of described growth and breeding is that P is only designated as in populationtQuantum weeds can produce seed,
Pi tFilial generation quantum weeds in the maximum filial generation quantum weeds of fitness be designated asWhen iteration starts
For current quantum weeds, seed number caused by every plant of quantum weedsDetermined by simulating Quantum rotating gate, wherein, in t generations
The transient state quantum anglec of rotation corresponding to the jth dimension variable of i-th plant of quantum weedsIt is defined as
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<mn>1</mn>
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<msub>
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</mrow>
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<mi>c</mi>
<mn>3</mn>
</msub>
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<mi>c</mi>
<mn>4</mn>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>r</mi>
<mn>2</mn>
</msub>
<mo>)</mo>
</mrow>
<mrow>
<mo>(</mo>
<msubsup>
<mi>G</mi>
<mi>j</mi>
<mi>t</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>Z</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
J=1,2 ..., 4N wherein c1,c2,c3,c4It is fixed constant for twiddle factor, r1,r2Between [0,1] it is uniform with
Machine number, the corresponding transient state quantum state of the jth dimension variable of i-th plant of quantum weeds are expressed as
<mrow>
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<mover>
<mi>q</mi>
<mo>&OverBar;</mo>
</mover>
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<mn>1</mn>
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<msubsup>
<mi>q</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
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<mi>t</mi>
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<mo>&times;</mo>
<mi>c</mi>
<mi>o</mi>
<mi>s</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>&theta;</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msqrt>
<mrow>
<mn>1</mn>
<mo>-</mo>
<msup>
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<mo>(</mo>
<msubsup>
<mi>q</mi>
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<mi>i</mi>
<mi>j</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
<mo>&times;</mo>
<mi>s</mi>
<mi>i</mi>
<mi>n</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>&theta;</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
Transient state quantum state residing for i-th plant of quantum weeds plant is defined as
<mrow>
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<mover>
<mi>q</mi>
<mo>&OverBar;</mo>
</mover>
<mi>i</mi>
<mrow>
<mi>t</mi>
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<mn>1</mn>
</mrow>
</msubsup>
<mo>=</mo>
<msubsup>
<mover>
<mi>q</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mn>1</mn>
</mrow>
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<mi>t</mi>
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<mn>1</mn>
</mrow>
</msubsup>
<mo>+</mo>
<msubsup>
<mover>
<mi>q</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mn>2</mn>
</mrow>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mn>...</mn>
<mo>+</mo>
<msubsup>
<mover>
<mi>q</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mn>4</mn>
<mi>N</mi>
</mrow>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
Its caused seed numberBy rightMeasurement obtains：
<mrow>
<msubsup>
<mi>K</mi>
<mi>i</mi>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mn>2</mn>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>I</mi>
<mi>f</mi>
<mn>4</mn>
<mi>N</mi>
<mo>&CenterDot;</mo>
<msub>
<mi>r</mi>
<mn>3</mn>
</msub>
<mo><</mo>
<msubsup>
<mover>
<mi>q</mi>
<mo>&OverBar;</mo>
</mover>
<mi>i</mi>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>4</mn>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>I</mi>
<mi>f</mi>
<mn>4</mn>
<mi>N</mi>
<mo>&CenterDot;</mo>
<msub>
<mi>r</mi>
<mn>3</mn>
</msub>
<mo>&GreaterEqual;</mo>
<msubsup>
<mover>
<mi>q</mi>
<mo>&OverBar;</mo>
</mover>
<mi>i</mi>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>12</mn>
<mo>)</mo>
</mrow>
</mrow>
r3For the random number between [0,1].
8. a kind of small echo digital watermark embeddings based on quantum weeds optimizing mechanism of according to claim 1 and extraction side
Method, it is characterised in that：The process of described space diffusion is that filial generation quantum weeds are by caused by the flooding mechanism of space, measuring
Sub- weeds produce new quantum weeds using the space diffusion for simulating Quantum rotating gate completion quantum weeds, wherein i-th plant of quantum
The of weedsThe quantum rotation angle of the jth dimension variable of strain filial generation quantum weedsIt is defined as
Corresponding quantum state is expressed as
Wherein c1,c2,c3,c4,c5,c6It is fixed constant for twiddle factor, r4,r5,r6It is uniformly random between [0,1]
Number, r is [0, Mmax/ 4] random integers between,Represent residing for the jth dimension of r strain quantum weeds in parent quantum weeds
Quantum state.
9. a kind of small echo digital watermark embeddings based on quantum weeds optimizing mechanism of according to claim 1 and extraction side
Method, it is characterised in that：The fitness process of quantum weeds is caused by described calculating is new, and new produce is calculated according to (3) and (4)
Quantum weeds fitness, it is competing each other for i-th plant of quantum weeds and its filial generation quantum weeds, first filial generation quantum weeds
Strive, the maximum filial generation quantum weeds of fitness, which are retained, to be denoted asOrderThenAnd Pi tCompetition, if's
Fitness value is more than Pi tFitness value, thenInstead of Pi tAs parent weeds P of new generationi t+1, otherwise Pi tIt is updated to Pi t+1。
All Pi tAfter completing renewal, last Pi t+1The maximum quantum weeds renewal G of middle fitnesst+1。
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| CN109214974A (en) * | 2018-08-31 | 2019-01-15 | 广州洪荒智能科技有限公司 | A kind of portrait photo's processing method based on digital watermarking |
| CN109712204A (en) * | 2018-11-30 | 2019-05-03 | 大连理工大学 | A kind of image encryption method based on FCM cluster |
| CN111476701A (en) * | 2020-03-18 | 2020-07-31 | 创驱（上海）新能源科技有限公司 | Digital image encryption method and decryption method based on Rijndael symmetric block encryption technology |
| CN114693500A (en) * | 2022-05-31 | 2022-07-01 | 山东科技大学 | Quantum image watermarking method for tampering detection and self-recovery |
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| CN103793874A (en) * | 2014-02-14 | 2014-05-14 | 中北大学 | Novel digital watermarking method capable of maintaining fidelity and robustness |
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